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Utilizing Software Engineering Education Support System ALECSS at an Actual Software Development Experiment: a Case Study

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Utilizing Software Engineering Education Support System ALECSS at an Actual Software Development Experiment: a Case Study Utilizing Software Engineering Education Support System ALECSS at an Actual Software Development Experiment: A Case Study Mika Ohtsuki and Tetsuro Kakeshita Department of Information Science, Saga University, Saga, Japan Keywords: Software Engineering Education, DevOps Tool, Software Verification, Coding Style Checking, Execution Checking, Static Checking, Peer Review. Abstract: We have proposed a software engineering education support system named ALECSS in our previous paper. ALECSS utilizes various DevOps tools such as Jenkins, Git, JUnit, Checkstyle and FindBugs to automatically check student’s programs from various viewpoints and to quickly provide feedbacks to the students. At the same time, ALECSS collects student’s log so that a teacher can easily observe the status of each student and/or each project team to improve software engineering education. In this paper, we utilize ALECSS at an actual software development experiment for self and peer review of the source code. Students are grouped into project teams and each student can view summary pages for the student or the team containing the messages generated by the DevOps tools integrated into ALECSS. We also collected feedback from the students and received many positive comments. 1 INTRODUCTION Evaluation Cycle Support System). ALECSS automatically checks source codes submitted by a It is quite important to utilize contemporary software students and/or a team from various viewpoints and development tools to realize practical software returns feedbacks to them. Various DevOps tools, engineering education (Nandigam, 2008). It is also such as JUnit, Git, Ant, Checkstyle, FidBugs and important to teach management of quality, cost and Jenkins, are utilized for the checking and for the delivery (QCD) of software development. integration of the checking tools. We also added Contemporary software engineering education original scripts to ALECSS for the checking of test requires collaboration of students as a team since the code and Git working status. A student or a team can software size is typically too large to work on a check their codes quickly and can improve them student. promptly by utilizing ALECSS. At the same time, the We are teaching collaborative software teacher can easily observe progress of the students development at the third academic year of our and the team. department majored in computer science. In our We utilized ALECSS to an actual software class, a student team faces with many problems. For development experiment in the 2018 spring semester. example, it is hard to see and control progress of the In this paper, we report the results of the experimental software development project without appropriate application. Although the students use ALECSS for sharing of the known problems and progress of the the first time, their evaluation of ALECSS is quite software development tasks at each source code good. among team members. It is also necessary to ensure In Section 2, we shall explain the major functions QCD of the software. In order to cope with such of ALECSS. We next explain the software problems, realistic software developers are shifting to development experiment in Section 3 and how to utilize various DevOps tools (Allspaw, 2009; Bass, utilize ALECSS in the experiment in Section 4. We 2015). present and discuss the result of our evaluation in In our previous paper (Ohtsuki, 2016), we Sections 5 and 6. In section 7, we show the related proposed a software engineering education support works and compare them with our contribution. system named ALECSS (Automated Learning and 367 Ohtsuki, M. and Kakeshita, T. Utilizing Software Engineering Education Support System ALECSS at an Actual Software Development Experiment: A Case Study. DOI: 10.5220/0007723203670375 In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 367-375 ISBN: 978-989-758-367-4 Copyright c 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved CSEDU 2019 - 11th International Conference on Computer Supported Education Figure 1: Entire structure of ALECSS. 2 SOFTWARE ENGINEERING student number or project name at several exercises. Therefore, it is necessary to have scripts to generate EDUCATION SUPPORT appropriate names from student number and/or SYSTEM ALECSS project name. Thus we can generate build.xml utilizing the scripts for each exercise of the ALECSS is developed using various DevOps tools experiment. such as Git, Jenkins, Ant, Checkstyle, FindBugs and JUnit as well as additional scripts for further checking 2.2 Coding Standard Checking of the student’s code. Figure 1 illustrates the entire structure and the behaviour of ALECSS. Coding standard checking ensures that the submitted A student can edit and submit a source program to Java program keeps one of the default coding style ALECSS by uploading the file(s) to the Git repository definitions (Sun Code Conventions and Google Java from the integrated development environment style). The checking is performed by executing Eclipse. Then Git notifies the file uploading to coding style checker Checkstyle. Execution of Jenkins. Jenkins automatically starts the build tool Checkstyle is defined as an Ant task by using the Ant by utilizing the submission as a trigger. Ant is taskdef clause in build.xml. controlled by the configuration file build.xml which contains setup and execution commands of the 2.3 Compile Checking various DevOps tools. The checking results are collected by Jenkins at the activity log. Then the Compile checking is a prerequisite of all other student and student team can browse the checking checking. ALECSS executes a standard compilation result and the status report to improve their source by invoking the javac command as an Ant task and code. The teacher can also browse the same report to can show the compilation result of the submitted Java understand status of each project. program. If the compilation fails, students can Various types of evaluation criteria can be browse the error messages on Jenkins. automatically checked using ALECSS. The criteria and the implementation of the checking mechanism 2.4 Output Result Checking are explained at the rest of this section. The output result checking is performed to ensure that 2.1 File Structure Checking the output of the submitted program matches to the output defined by the specification. ALECSS We can perform the existence checking of the executes a compiled Java program and record the required files by using condition tag and available tag output to the log which can be observed on Jenkins. defined as Ant tasks. File Structure Checking is Furthermore, we prepare a script to compare the required for all exercises and the file names are output log with a predefined file. different depending on the exercise. Furthermore, file Such script can be implemented by using the diff and folder names are assigned depending on the command if the result is fixed. For the case that the 368 Utilizing Software Engineering Education Support System ALECSS at an Actual Software Development Experiment: A Case Study output depends on student number, student name or 2.9 Static Code Checking project name, we develop a special script using the same technique as explained for File Structure Static code analysis tool FindBugs is utilized for the Checking. checking to detect pitfalls which can be observed within a Java source code. Execution of FindBugs is 2.5 Git Work Execution Checking defined as an Ant task as in the case of Checkstyle. Git maintains a commitment log storing four types of Git actions: file addition (add and commit), file 3 COOPERATIVE SOFTWARE deletion (remove and commit), file update (edit and commit), revert (return to a former commitment DEVELOPMENT state). The Git work execution checking examines EXPERIMENT the Git log to confirm that a student correctly performs the required Git operations. The Our cooperative software development experiment is commitment log can be accessed by the Git log provided for the undergraduate student at the third command. We are developing a script for the Git academic year. The experiment is a compulsory work execution checking. subject for graduation and usually about 60 students are enrolled to the experiment each year. Our depart- 2.6 JUnit Execution Checking ment is accredited as a computer science program and the students have learned software engineering and JUnit is utilized to check whether the subroutines in basic Java programming before the experiment. The the submitted program return correct values. experiment consists of fifteen weeks of 3 hour Teachers need to provide a set of test codes executed exercises. Table 1 represents the experiment plan. by JUnit for the checking. JUnit can be executed as an Ant task with a junit tag. We can obtain the log Table 1: Experimental plan. using the task and can observe the number of Week Description successes/failures of the unit test on Jenkins. 1 Setting up software development environment (Git and Eclipse) 2.7 Test Case Null Implementation 2 Git Exercise Checking 3 Java Exercise (including Checkstyle and Javadoc) Our experiment also contains exercises to develop 4-5 JUnit Exercise 6 Introduction to Group Exercise (Group test code. If a student develops an empty test code, formation, Ice Breaking and Explanation of any test will succeed in the JUnit framework. The test Requirements) case null implementation checking detects such 7-9 Implementation (First Iteration) empty test cases. This can be implemented by Introduction to ALECSS (at Week 9) counting the number of lines in each test case method. 10 Peer Review (First Iteration) We have developed a Java language parser utilizing 11-13 Explanation of Additional Requirements JavaCC to extract a test case method and a script Implementation (Second Iteration) counting the number of lines of the test case method Student Survey (at Week 12) (Koga, 2018). The parser and the script are defined 14 Peer Review (Second Iteration) as Ant tasks for automatic execution.
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